Inferential sensor for internal heat exchanger parameters

ABSTRACT

Methods, devices, and systems for an inferential sensor for internal heat exchanger parameters are described herein. One device includes a memory, and a processor configured to execute executable instructions stored in the memory to receive a number of measured process variables of a heat exchanger, including a number of measured inlet process variables and a number of measured outlet process variables, predict, using a dynamic differential model including the number of measured inlet process variables, internal parameters of the heat exchanger and a number of outlet process variables of the heat exchanger, compare the number of measured outlet process variables with the number of predicted outlet process variables, and update, based on the comparison, the internal parameters of the heat exchanger.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims foreign priority to EP Application No.15193498.1 filed Nov. 6, 2015, the specification of which is hereinincorporated by reference.

TECHNICAL FIELD

The present disclosure relates to methods, devices, and systems for aninferential sensor for internal heat exchanger parameters.

BACKGROUND

Adaptation of heat exchanger model parameters may be necessary in orderfor precise model-based control and optimization of thermal processes ofthe heat exchanger in heating and cooling applications. Due to ageingphenomena like fouling, frost formation etc. the actual parameters ofheat exchangers can differ from nominal parameters of nominal heatexchangers. For example, knowing the actual parameters of a heatexchanger used in heat pump and/or air-conditioning applications canallow for more efficient operation of the heat pump and/orair-conditioning systems that can be subject to ageing phenomena.

Steady state models based on the logarithmic mean temperature differenceconcept can provide sufficient accuracy to estimate the parameters ofthe heat exchanger in steady state conditions. However, for practicalapplications it may be necessary to estimate the internal state andparameters of the heat exchanger continuously under time varyingoperating conditions. Continuous estimation of the internal state andparameters under time varying conditions requires an accurate dynamicmodel.

Dynamic models based on finite element or finite volume approximation ofdistributed parameter models described by partial differential equationsrequire high order approximation to achieve sufficient accuracy of theparameters and state estimates, as well as consistency with thelogarithmic mean temperature model in steady state conditions.Therefore, dynamic models are not applicable for embedded controllersand optimization methods implemented in microcontrollers with limitedcomputational power and memory. Currently available methods, devices,and systems that can provide sufficient steady state and dynamicaccuracy may exceed the processing and memory resource capabilities ofcurrent microcontrollers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for an inferential sensor for internal heatexchanger parameters, in accordance with one or more embodiments of thepresent disclosure.

FIG. 2 illustrates a system for an inferential sensor for internal heatexchanger parameters, in accordance with one or more embodiments of thepresent disclosure.

FIG. 3 is a flow chart of a method for an inferential sensor forinternal heat exchanger parameters, in accordance with one or moreembodiments of the present disclosure.

FIG. 4 is a schematic block diagram of a controller for an inferentialsensor for internal heat exchanger parameters, in accordance with one ormore embodiments of the present disclosure.

DETAILED DESCRIPTION

Methods, devices, and systems for an inferential sensor for internalheat exchanger parameters are described herein. For example, one or moreembodiments include a memory, and a processor configured to executeexecutable instructions stored in the memory to receive a number ofmeasured process variables of the heat exchanger, including a number ofmeasured inlet process variables and a number of measured outlet processvariables. The processor can further execute executable instructionsstored in the memory to predict, using a dynamic differential modelincluding the number of measured inlet process variables, internalparameters of the heat exchanger and a number of outlet processvariables of the heat exchanger. The processor can additionally executeexecutable instructions stored in the memory to compare the number ofmeasured outlet process variables with the number of predicted outletprocess variables, and update, based on the comparison, the internalparameters of the heat exchanger.

An inferential sensor for internal heat exchanger parameters, inaccordance with the present disclosure, can be based on a dynamic loworder model that can provide an accurate approximation of a dynamicresponse of the heat exchanger in transient conditions, as well as beconsistent in steady state conditions. The low order dynamic model canbe used to estimate changes of heat exchanger parameters (e.g., heatexchange surface temperatures, heat transfer coefficient, etc.) andpredict outlet process variables.

Maintaining accurate inferential measurements of heat exchangerparameters using the low order dynamic model of a heat exchanger canbring significant economic benefits. For example, the low order dynamicmodel can be used to ensure efficient heat exchanger operation that maylead to utility cost savings, as well as ensuring the heat exchanger isfunctioning properly.

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof. The drawings show, by wayof illustration, how one or more embodiments of the disclosure may bepracticed.

These embodiments are described in sufficient detail to enable those ofordinary skill in the art to practice one or more embodiments of thisdisclosure. It is to be understood that other embodiments may beutilized and that process, electrical, and/or structural changes may bemade without departing from the scope of the present disclosure.

As will be appreciated, elements shown in the various embodiments hereincan be added, exchanged, combined, and/or eliminated so as to provide anumber of additional embodiments of the present disclosure. Theproportion and the relative scale of the elements provided in thefigures are intended to illustrate the embodiments of the presentdisclosure, and should not be taken in a limiting sense.

The figures herein follow a numbering convention in which the firstdigit or digits correspond to the drawing figure number and theremaining digits identify an element or component in the drawing.

As used herein, “a” or “a number of” something can refer to one or moresuch things. For example, “a number of process variables” can refer toone or more process variables.

FIG. 1 illustrates a system 100 for an inferential sensor for internalheat exchanger 104 parameters, in accordance with one or moreembodiments of the present disclosure. As shown in FIG. 1, the system100 can include heat pump 102, heat exchanger 104, controller 106, anumber of measured temperatures 108, a number of measured pressures 110,and a number of measured flow rates 112.

Controller 106 can receive, from heat exchanger 104, a number ofmeasured process variables of heat exchanger 104. The number of measuredprocess variables can include measured temperatures 108, measuredpressures 110, and/or measured flow rates 112. The measured temperatures108 can include measured inlet and outlet temperatures. Additionally,the measured pressures 110 can include measured inlet and outletpressures. Further, the measured flow rates 112 can include a measuredinlet and/or outlet flow rate.

As used herein, heat exchanger 104 can be a device that allows for theprocess of heat exchange (e.g., the transfer of thermal energy) betweentwo media that are at different temperatures and separated by a solidwall (e.g., a heat exchange surface) to prevent the two media frommixing. For example, heat exchanger 104 can be a concentric tube heatexchanger with a parallel flow or a counter-flow arrangement. As anotherexample, heat exchanger 104 can be a cross-flow heat exchanger that canbe finned or un-finned.

As used herein, a heat exchange surface can be a medium through which ahot fluid of heat exchanger 104 can transfer heat to a cold fluid ofheat exchanger 104. The heat transfer coefficient between the cold fluidof heat exchanger 104 and the hot fluid of heat exchanger 104 can beaffected by the heat transfer from the hot fluid to the heat exchangesurface, thermal resistance between the heat exchange surface, and heattransfer from the heat exchange surface to the cold fluid of heatexchanger 104.

Although heat exchanger 104 is described as being a concentric tube heatexchanger or a cross-flow heat exchanger, embodiments of the presentdisclosure are not so limited. For example, heat exchanger 104 can beany other type of heat exchanger.

Controller 106 can be part of heat pump 102. As used herein, heat pump102 can be a device that moves thermal energy by absorbing heat from acold space and releasing it into a warmer space. For example, heat pump102 can utilize heat exchanger 104 to move thermal energy from a coldspace to a working fluid (e.g., cold fluid of heat exchanger 104) andrelease from a working fluid in a different thermodynamic state to awarm space. Thermal energy is typically transported using a medium suchas a liquid, vapor, and/or a mixture of the two.

Although controller 106 is shown in FIG. 1 as part of heat pump 102,embodiments of the present disclosure are not so limited. For example,controller 106 can be located remotely from heat pump 102 and canreceive a number of process variables of heat exchanger 104 via a wiredor wireless network relationship.

The wired or wireless network can be a network relationship thatconnects heat pump 102 to controller 106. Examples of such a networkrelationship can include a serial communication line, and/or a localarea network (LAN). Data from heat pump 102 can further be communicatedto a distributed computing environment (e.g., a cloud computingenvironment), and/or the Internet using a wide area network (WAN), amongother types of network relationships.

The number of measured inlet process variables can include an inlettemperature of the hot fluid of heat exchanger 104 and an inlettemperature of a cold fluid of heat exchanger 104. The number ofmeasured outlet process variables can include an outlet temperature ofthe hot fluid of heat exchanger 104 and an outlet temperature of thecold fluid of heat exchanger 104. The inlet temperature of the hot fluidof heat exchanger 104 can be different than the outlet temperature ofthe hot fluid of heat exchanger 104 as the hot fluid transfers heat to acold fluid of heat exchanger 104 as the hot fluid moves the length ofheat exchanger 104. Further, the inlet temperature of the cold fluid ofheat exchanger 104 can be different than the outlet temperature of thehot fluid of heat exchanger 104 as the cold fluid receives thermalenergy (e.g., heat) from the hot fluid as the cold fluid moves thelength of heat exchanger 104.

Although the number of measured inlet and outlet process variables aredescribed as including an inlet and outlet temperature of the hot fluidof heat exchanger 104 and an inlet and outlet temperature of a coldfluid of heat exchanger 104, embodiments of the disclosure are not solimited. For example, the number of measured inlet and outlet processvariables can include an inlet and outlet temperature of the hot fluidof heat exchanger 104, an inlet temperature of a cold fluid of heatexchanger 104, but not an outlet temperature of a cold fluid of heatexchanger 104, an inlet temperature of the hot fluid of heat exchanger104, an inlet and outlet temperature of a cold fluid of heat exchanger104, but not an outlet temperature of a hot fluid of heat exchanger 104,or any other combination thereof.

As used herein, the hot fluid of heat exchanger 104 can be any fluidsuitable to enable the transfer of heat from one medium (e.g., hotfluid) to another medium (e.g., cold fluid). For example, the hot fluidcan be water, oil, ammonia, alcohol, and/or any combination thereof in aliquid or vapor state, although embodiments of the present disclosureare not so limited.

As used herein, the cold fluid of the heat exchanger can be any fluidsuitable to enable the transfer of heat from one medium (e.g., hotfluid) to another medium (e.g., cold fluid). For example, the cold fluidcan be water, oil, ammonia, alcohol, and/or any combination thereof in aliquid or vapor state, although embodiments of the present disclosureare not so limited.

The number of measured inlet process variables can include an inlet flowrate of the hot fluid of heat exchanger 104 and an inlet flow rate ofthe cold fluid of heat exchanger 104. The number of measured outletprocess variables can include an outlet flow rate of the hot fluid ofheat exchanger 104 and an outlet flow rate of the cold fluid of heatexchanger 104. For example, the flow rates of the hot fluid and/or thecold fluid of heat exchanger 104 can be a flow rate for optimal heattransfer in heat exchanger 104.

Although the number of measured inlet and outlet process variables aredescribed as including an inlet flow rate of the hot fluid of heatexchanger 104, an inlet flow rate of the cold fluid of heat exchanger104, an outlet flow rate of the hot fluid of heat exchanger 104, and anoutlet flow rate of the cold fluid of heat exchanger 104, respectively,embodiments of the present disclosure are not so limited. For example,the number of measured inlet and outlet process variables can include aninlet flow rate of the hot fluid of heat exchanger 104, an inlet flowrate of the cold fluid of heat exchanger 104, and an outlet flow rate ofthe hot fluid of heat exchanger 104, among other combinations ofmeasured inlet and outlet flow rates.

The number of measured inlet process variables can include an inletpressure of the hot fluid of heat exchanger 104 and an inlet pressure ofthe cold fluid of heat exchanger 104. The number of measured outletprocess variables can include an outlet pressure of the hot fluid ofheat exchanger 104 and an outlet pressure of the cold fluid of heatexchanger 104. For example, the pressures of the hot fluid and/or thecold fluid of heat exchanger 104 can be between 2 and 18 barA, althoughembodiments of the present disclosure are not so limited.

Although the number of measured inlet and outlet process variables aredescribed as including an inlet and outlet pressure of the hot fluid ofheat exchanger 104 and an inlet and outlet pressure of a cold fluid ofheat exchanger 104, embodiments of the disclosure are not so limited.For example, the number of measured inlet and outlet process variablescan include an inlet and outlet pressure of the hot fluid of heatexchanger 104, an inlet pressure of a cold fluid of heat exchanger 104,but not an outlet pressure of a cold fluid of heat exchanger 104, aninlet pressure of the hot fluid of heat exchanger 104, an inlet andoutlet pressure of a cold fluid of heat exchanger 104, but not an outletpressure of a hot fluid of heat exchanger 104, or any other combinationthereof.

Controller 106 can predict, using a dynamic differential model includingthe number of measured inlet process variables, internal parameters ofheat exchanger 104 and a number of outlet process variables of heatexchanger 104.

Internal parameters of heat exchanger 104 can include a heat transfercoefficient of heat exchanger 104. The heat transfer coefficient candescribe the heat transfer that can occur in heat exchanger 104. Theheat transfer coefficient of heat exchanger 104 may be less than anominal heat transfer coefficient of a nominal heat exchanger. As usedherein, a nominal heat exchanger can be a heat exchanger that does notexperience losses due to fouling and/or other environmental factors.

For example, heat exchanger 104 may experience fouling due to rust ormineral deposits on the heat exchange surface of heat exchanger 104 thatcan cause higher thermal resistance to heat transfer in heat exchanger104. As a result, a heat transfer coefficient of heat exchanger 104 maybe less than the nominal heat transfer coefficient of the nominal heatexchanger.

Internal parameters of heat exchanger 104 can include metal temperaturesof a heat exchange surface of heat exchanger 104. Temperatures of a heatexchange surface within heat exchanger 104 may not be easily measured.Controller 106 can therefore predict the metal temperatures of the heatexchange surface of heat exchanger 104 to calculate an efficiency ofheat exchanger 104, as will be further described herein.

Controller 106 can determine an amount of heat transfer from the hotfluid to the cold fluid of heat exchanger 104 using a logarithmic meantemperature of the number of measured inlet process variables of heatexchanger 104 and a number of predicted outlet process variables of heatexchanger 104. For example, controller 106 can utilize the measuredinlet temperatures of the hot and cold fluids of heat exchanger 104, themeasured inlet pressures of the hot and cold fluids of heat exchanger104, the measured inlet flow rates of the hot and cold fluids of heatexchanger 104, predicted outlet temperatures of the hot and cold fluidsof heat exchanger 104, predicted outlet pressures of the hot and coldfluids of heat exchanger 104, and predicted outlet flow rates of the hotand cold fluids of heat exchanger 104 to determine an amount of heattransfer from the hot fluid to the cold fluid of heat exchanger 104using a logarithmic mean temperature.

Differential equations for the inlet temperatures and outlettemperatures of the hot fluid of heat exchanger 104, heat exchangesurface of heat exchanger 104, and the cold fluid of heat exchanger 104can be developed based on the heat transfer during transient conditionsbeing characterized by the logarithmic mean temperature of the hotfluid, the cold fluid, and the heat exchange surface of heat exchanger104, energy and mass conservation for the hot fluid of heat exchanger104 and the cold fluid of heat exchanger 104, as well as energyconservation for the heat exchange surface of heat exchanger 104. Asused herein, transient conditions can refer to internal state andparameters of heat exchanger 104 changing continuously under timevarying operating conditions.

The logarithmic mean temperature used in steady state models can bedescribed by equation 1:

$\begin{matrix}{{\Delta \; T_{LMTD}} = {\frac{{\Delta \; T_{1}} - {\Delta \; T_{2}}}{{\ln \; \Delta \; T_{1}} - {\ln \; \Delta \; T_{2}}} = {{LMTD}\left( {{\Delta \; T_{1}},{\Delta \; T_{2}}} \right)}}} & (1)\end{matrix}$

where ΔT_(LMTD) describes the logarithmic mean temperature, and ΔT₁ andΔT₂ are mean temperatures of the inlet and outlet of the hot cold andcold fluids of heat exchanger 104.

Utilizing the logarithmic mean temperature equation (e.g., equation 1),the mean temperature of the hot fluid of heat exchanger 104 (e.g.,equation 2), the mean temperature of the metal of the heat exchangesurface of heat exchanger 104 (e.g., equation 3), and the meantemperature of the cold fluid of heat exchanger 104 (e.g., equation 4)can be obtained as a logarithmic mean temperature between the inlet sideand outlet side of heat exchanger 104 and zero reference temperature:

T _(h) =LMTD(T _(h1) ,T _(h2))  (2)

T _(m) =LMTD(T _(m1) ,T _(m2))  (3)

T _(c) =LMTD(T _(c1) ,T _(c2))  (4)

where T_(h) is the logarithmic mean temperature of the hot fluid of heatexchanger 104, T_(m) is the logarithmic mean temperature of the metal ofthe heat exchange surface of heat exchanger 104, and T_(c) is thelogarithmic mean temperature of the cold fluid of heat exchanger 104.T_(h), T_(m), and T_(c) can be calculated as logarithmic meantemperature differences between the fluid inlet and outlet temperatures,and a reference zero temperature.

A heat and mass balance of the hot fluid, the cold fluid, and the metaltemperature of the heat exchange surface of heat exchanger 104 resultsin the following differential equations (e.g., equations 5-7) describingthe internal parameters (e.g., boundary conditions for temperaturesrelated to the hot fluid, heat exchanger surface, and cold fluid of heatexchanger 104) T_(h1), T_(h2), T_(m1), T_(m2), T_(c1), T_(c2) as theychange with time:

$\begin{matrix}{{m_{h}{c_{h}\left( {{\frac{\partial T_{h}}{\partial T_{h\; 1}}\frac{T_{h\; 1}}{t}} + {\frac{\partial T_{h}}{\partial T_{h\; 2}}\frac{T_{h\; 2}}{t}}} \right)}} = {{F_{h}{c_{h}\left( {T_{h\; 1} - T_{h\; 2}} \right)}} - {\alpha_{h}{A_{h}\left( {T_{h} - T_{m}} \right)}}}} & (5) \\{{m_{m}{c_{m}\left( {{\frac{\partial T_{m}}{\partial T_{m\; 1}}\frac{T_{m\; 1}}{t}} + {\frac{\partial T_{m}}{\partial T_{m\; 2}}\frac{T_{m\; 2}}{t}}} \right)}} = {{\alpha_{h}{A_{h}\left( {T_{h} - T_{m}} \right)}} - {\alpha_{c}{A_{c}\left( {T_{m} - T_{c}} \right)}}}} & (6) \\{{m_{c}{c_{c}\left( {{\frac{\partial T_{c}}{\partial T_{c\; 1}}\frac{T_{c\; 1}}{t}} + {\frac{\partial T_{c}}{\partial T_{c\; 2}}\frac{T_{c\; 2}}{t}}} \right)}} = {{F_{c}{c_{c}\left( {T_{c\; 2} - T_{c\; 1}} \right)}} + {\alpha_{c}{A_{c}\left( {T_{m} - T_{c}} \right)}}}} & (7)\end{matrix}$

where m_(h), m_(m), m_(c) denote individual masses of the hot fluid,heat exchange surface, and cold fluid of heat exchanger 104,respectively, c_(h), c_(m), c_(c) denote the specific heats of the hotfluid, heat exchange surface, and cold fluid of heat exchanger 104,respectively, F_(h), F_(c) denote the mass flows of the hot fluid andcold fluid of heat exchanger 104, respectively, A_(h), A_(c) are surfaceareas between the heat exchange surface and hot fluid or cold fluid ofheat exchanger 104, and α_(h), α_(c) are heat transfer coefficientsbetween the heat exchange surface and hot fluid or cold fluid of heatexchanger 104.

The differential equations represented by equations 5-7 can be used toevaluate internal parameters of heat exchanger 104. The internalparameters of heat exchanger 104 can correspond to individual inlet andoutlet temperatures of the hot fluid, heat exchange surface, and coldfluid of heat exchanger 104.

The dynamic model is fully consistent with the logarithmic meantemperature model in steady state and transient conditions, as well asobserves mass and energy conservation laws. The dynamic model can beused to predict internal parameters of heat exchanger 104, such as aheat transfer coefficient and other parameters for optimization (e.g.,thermal cycle performance optimization) in steady state, as well asduring transient conditions.

Controller 106 can calculate an efficiency of heat exchanger 104 usingthe predicted internal parameters of heat exchanger 104 and the numberof measured process variables of heat exchanger 104. That is, theefficiency of heat exchanger 104 can be calculated using the internalparameters of heat exchanger 104 and the number of measured inletprocess variables and the number of measured outlet process variables.

Controller 106 can compare the number of measured outlet processvariables with the number of predicted outlet process variables. Forexample, the measured outlet temperatures of the hot and cold fluids ofheat exchanger 104 can be compared to the predicted outlet temperaturesof the hot and cold fluids of heat exchanger 104. Additionally, themeasured outlet pressures of the hot and cold fluids of heat exchanger104 can be compared to the predicted outlet pressures of the hot andcold fluids of heat exchanger 104. Further, the measured outlet flowrates of the hot and cold fluids of heat exchanger 104 can be comparedto the predicted outlet flow rates of the hot and cold fluids of heatexchanger 104. The internal parameters of heat exchanger 104 may changeduring operation of heat exchanger 104, resulting in a change in themeasured outlet process variables. The change can result from a changeof the internal parameters of heat exchanger 104.

In some embodiments, fouling can result in a change in the internalparameters of heat exchanger 104. Fouling can occur when impurities,rust, and/or other deposits occur on the heat exchange surface of a heatexchanger (e.g., heat exchanger 104). For example, the hot fluid and/orcold fluid of heat exchanger 104 can include impurities such as mineralsand/or other contaminants that can deposit onto a heat exchange surfaceof heat exchanger 104, causing a decrease in the amount of heattransferred from the hot fluid to the cold fluid. The decrease in heattransfer is due to a higher thermal resistance of the heat transfersurface as a result of fouling. As another example, frost can occur onthe heat exchange surface between the working fluid and air when airmoisture condenses on the heat exchange surface and freezes. The frostcan act as an insulator that may cause a decrease in the amount of heattransfer of heat exchanger 104.

Controller 106 can update, based on the comparison of the number ofmeasured outlet process variables with the number of predicted outletprocess variables, the internal parameters of heat exchanger 104. Thatis, if the actual internal parameters of heat exchanger 104 have changed(e.g., due to fouling), the predicted internal parameters of heatexchanger 104 can be updated based on the comparison.

For example, if the actual heat transfer coefficient of heat exchanger104 is smaller than the predicted heat transfer coefficient, the loworder dynamic model can predict a lower outlet temperature of the coldfluid of heat exchanger 104. Knowing this difference, the low orderdynamic model can update the heat transfer coefficient of heat exchanger104.

In some embodiments, heat exchanger 104 can be a liquid-liquid heatexchanger. For example, the hot fluid and cold fluid of heat exchanger104 can remain in a liquid state throughout the heat exchange process.That is, no boiling and/or evaporation of the hot and/or cold fluid ofheat exchanger 104 occurs during the heat exchange process in heatexchanger 104. The liquid-liquid heat exchanger can be accuratelymodeled using the number of measured process variables of heat exchanger104.

In some embodiments, heat exchanger 104 can be a phase change heatexchanger. A phase change heat exchanger can include partial boilingand/or evaporation of a liquid of heat exchanger 104 (e.g., the hotfluid or the cold fluid).

Heat exchanger 104 can additionally be accurately modeled as a phasechange heat exchanger using a logarithmic mean temperature of the liquidportion of heat exchanger 104 and a logarithmic mean temperature of thea boiling and/or evaporating portion of heat exchanger 104. Theimplementation of the inferential sensor for internal heat exchangerparameters can utilize other thermodynamic state variables, such asenthalpies of individual fluids (e.g., hot fluid and cold fluids) ofheat exchanger 104.

FIG. 2 illustrates a system 214 for an inferential sensor for internalheat exchanger 218 parameters, in accordance with one or moreembodiments of the present disclosure. As shown in FIG. 2, the system214 can include air-conditioner 216, heat exchanger 218, controller 206,a number of measured temperatures 220, a number of measured pressures222, and a number of measured flow rates 224.

Similar to the embodiment described in FIG. 1, controller 206 canreceive, from heat exchanger 218, a number of process variables of heatexchanger 218. The number of measured process variables of heatexchanger 218 can include a measured temperatures 220, measuredpressures 222, and/or measured flow rates 224. The measured temperatures220 can include measured inlet and outlet temperatures. Additionally,the measured pressures 222 can include measured inlet and outletpressures. Further, the measured flow rates 224 can include a measuredinlet and/or outlet flow rate.

Controller 206 can be part of air-conditioner 216. As used herein,air-conditioner 216 can be a device that lowers the air temperature of aspace. Air-conditioner 216 can lower the air temperature using heatexchanger 218.

Although controller 206 is shown in FIG. 2 as part of air-conditioner216, embodiments of the present disclosure are not so limited. Forexample, controller 206 can be located remotely from air-conditioner 216and can receive a number of process variables of heat exchanger 218 viaa wired or wireless network relationship.

The wired or wireless network can be a network relationship thatconnects air-conditioner 216 to controller 206. Examples of such anetwork relationship can include a serial communication line, and/or alocal area network (LAN). Data from air-conditioner 216 can further becommunicated to a distributed computing environment (e.g., a cloudcomputing environment), and/or the Internet using a wide area network(WAN), among other types of network relationships.

Controller 206 can determine an amount of heat transfer from the hotfluid to the cold fluid of air-conditioner 216 using a logarithmic meantemperature of the number of measured inlet process variables ofair-conditioner 216 and a number of predicted outlet process variablesof air-conditioner 216. For example, controller 206 can utilize themeasured inlet temperatures of the hot and cold fluids ofair-conditioner 216, the measured inlet pressures of the hot and coldfluids of air-conditioner 216, the measured inlet flow rates of the hotand cold fluids of air-conditioner 216, predicted outlet temperatures ofthe hot and cold fluids of air-conditioner 216, predicted outletpressures of the hot and cold fluids of air-conditioner 216, andpredicted outlet flow rates of the hot and cold fluids ofair-conditioner 216 to determine an amount of heat transfer from the hotfluid to the cold fluid of heat exchanger 218 using a logarithmic meantemperature.

Controller 206 can compare the number of measured outlet processvariables with the number of predicted outlet process variables. Forexample, the measured outlet temperatures of the hot and cold fluids ofheat exchanger 218 can be compared to the predicted outlet temperaturesof the hot and cold fluids of heat exchanger 218. Additionally, themeasured outlet pressures of the hot and cold fluids of heat exchanger218 can be compared to the predicted outlet pressures of the hot andcold fluids of heat exchanger 218. Further, the measured outlet flowrates of the hot and cold fluids of heat exchanger 218 can be comparedto the predicted outlet flow rates of the hot and cold fluids of heatexchanger 218. The internal parameters of heat exchanger 218 may changeduring operation of heat exchanger 218, resulting in a change in themeasured outlet process variables. The change can result from a changeof the internal parameters of heat exchanger 218.

Controller 206 can update, based on the comparison of the number ofmeasured outlet process variables with the number of predicted outletprocess variables, the internal parameters of heat exchanger 218. Forexample, the internal parameters of heat exchanger 218 can be updatedbased on the comparison.

FIG. 3 is a flow chart of a method 325 for an inferential sensor forinternal heat exchanger parameters, in accordance with one or moreembodiments of the present disclosure. Method 325 can be performed by,for example, controllers 106, 206, and 406, as described in connectionwith FIGS. 1, 2, and 4, respectively.

At block 326 of method 325, the controller can receive a number ofmeasured process variables of the heat exchanger (e.g., heat exchanger104, 218, previously described in connection with FIGS. 1 and 2,respectively). For example, the controller can receive a number ofmeasured temperatures (e.g., measured temperatures 108, 220, previouslydescribed in connection with FIGS. 1 and 2, respectively) of the heatexchanger, a number of measured pressures (e.g., number of measuredpressures 110, 222, previously described in connection with FIGS. 1 and2, respectively) of the heat exchanger, and a number of measured flowrates (e.g., number of measured flow rates 112, 224, as previouslydescribed in connection with FIGS. 1 and 2, respectively).

At block 328 of method 325, the controller can predict internalparameters of the heat exchanger and a number of outlet processvariables by a dynamic differential model using a heat and mass balanceof the number of measured inlet process variables and the number ofmeasured outlet process variables of the heat exchanger. For example,controller 106 can utilize a heat and mass balance (e.g., equations 5-7,previously described in connection with FIG. 1) of the measured inlettemperatures of the hot and cold fluids of the heat exchanger, measuredinlet pressures of the hot and cold fluids of the heat exchanger,measured inlet flow rates of the hot and cold fluids of the heatexchanger, predicted outlet temperatures of the hot and cold fluids ofthe heat exchanger, predicted outlet pressures of the hot and coldfluids of the heat exchanger, and predicted outlet flow rates of the hotand cold fluids of the heat exchanger to predict internal variables ofthe heat exchanger.

At block 330 of method 325, the controller can compare the number ofmeasured outlet process variables with the number of predicted outletprocess variables. That is, the controller can use the heat exchangermodel to compare the measured outlet process variables with the valuespredicted by the model. The controller can use the difference to updatemodel parameters that define the heat transfer of the heat exchanger.

At block 332 of method 325, the controller can update, based on thecomparison of the measured outlet process variables with the valuespredicted by the model, the internal parameters of the heat exchanger.For example, if the actual internal parameters of heat exchanger 104have changed (e.g., due to fouling, etc.), the internal parameters ofthe heat exchanger can be updated based on the comparison.

The controller can update a heat transfer coefficient of the heatexchanger. For example, a predicted heat transfer coefficient of theheat exchanger can be updated in response to a change in the actual heattransfer coefficient of the heat exchanger.

The controller can update metal temperatures of a heat exchange surfaceof the heat exchanger. For example, a predicted metal temperature of theheat exchange surface can be updated in response to a change in theactual metal temperature of the heat exchange surface of the heatexchanger.

Method 325 can be performed while the heat exchanger is operating. Forexample, method 325 can be performed during operation of the heatexchanger. That is, the heat exchanger does not need to be removed fromservice while method 325 is performed. Further, method 325 can becontinuously repeated during operation of the heat exchanger. That is,the method 325 can be continuously repeated to dynamically model andpredict changes of the heat exchanger, as well as continuously updatepredicted values (e.g., internal parameters and/or outlet processvariables).

FIG. 4 is a schematic block diagram of a controller 406 for aninferential sensor for internal heat exchanger parameters (e.g., heatexchanger 104, 218, previously described in connection with FIGS. 1 and2, respectively), in accordance with one or more embodiments of thepresent disclosure. Controller 406 can be, for example, controllers 106and 206, previously described in connection with FIGS. 1 and 2,respectively. Controller 406 can include a memory 436 and a processor434 configured for an inferential sensor for internal heat exchangerparameters in accordance with the present disclosure.

The memory 436 can be any type of storage medium that can be accessed bythe processor 434 to perform various examples of the present disclosure.For example, the memory 436 can be a non-transitory computer readablemedium having computer readable instructions (e.g., computer programinstructions) stored thereon that are executable by the processor 434 toreceive a number of process variables of the heat exchanger. Further,processor 434 can execute the executable instructions stored in memory436 to predict internal parameters of a heat exchanger and a number ofoutlet process variables of the heat exchanger, compare a number ofmeasured outlet process variables with the number of predicted outletprocess variables, and update, based on the comparison, the internalparameters of the heat exchanger.

The memory 436 can be volatile or nonvolatile memory. The memory 436 canalso be removable (e.g., portable) memory, or non-removable (e.g.,internal) memory. For example, the memory 436 can be random accessmemory (RAM) (e.g., dynamic random access memory (DRAM) and/or phasechange random access memory (PCRAM)), read-only memory (ROM) (e.g.,electrically erasable programmable read-only memory (EEPROM) and/orcompact-disc read-only memory (CD-ROM)), flash memory, a laser disc, adigital versatile disc (DVD) or other optical storage, and/or a magneticmedium such as magnetic cassettes, tapes, or disks, among other types ofmemory.

Further, although memory 436 is illustrated as being located withincontroller 406, embodiments of the present disclosure are not solimited. For example, memory 436 can also be located internal to anothercomputing resource (e.g., enabling computer readable instructions to bedownloaded over the Internet or another wired or wireless connection).

Although specific embodiments have been illustrated and describedherein, those of ordinary skill in the art will appreciate that anyarrangement calculated to achieve the same techniques can be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments of thedisclosure.

It is to be understood that the above description has been made in anillustrative fashion, and not a restrictive one. Combination of theabove embodiments, and other embodiments not specifically describedherein will be apparent to those of skill in the art upon reviewing theabove description.

The scope of the various embodiments of the disclosure includes anyother applications in which the above structures and methods are used.Therefore, the scope of various embodiments of the disclosure should bedetermined with reference to the appended claims, along with the fullrange of equivalents to which such claims are entitled.

In the foregoing Detailed Description, various features are groupedtogether in example embodiments illustrated in the figures for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the embodiments of thedisclosure require more features than are expressly recited in eachclaim.

Rather, as the following claims reflect, inventive subject matter liesin less than all features of a single disclosed embodiment. Thus, thefollowing claims are hereby incorporated into the Detailed Description,with each claim standing on its own as a separate embodiment.

What is claimed:
 1. An inferential sensor for internal heat exchangerparameters, comprising: a memory; a processor configured to executeexecutable instructions stored in the memory to: receive a number ofmeasured process variables of the heat exchanger, including: a number ofmeasured inlet process variables; and a number of measured outletprocess variables; predict, using a dynamic differential model includingthe number of measured inlet process variables: internal parameters ofthe heat exchanger; and a number of outlet process variables of the heatexchanger; compare the number of measured outlet process variables withthe number of predicted outlet process variables; and update, based onthe comparison, the internal parameters of the heat exchanger.
 2. Theinferential sensor of claim 1, wherein the number of measured inletprocess variables include: an inlet temperature of the hot fluid of theheat exchanger; an inlet temperature of the cold fluid of the heatexchanger; an inlet pressure of the hot fluid of the heat exchanger; aninlet pressure of the cold fluid of the heat exchanger; an inlet flowrate of the hot fluid of the heat exchanger; and an inlet flow rate ofthe cold fluid of the heat exchanger.
 3. The inferential sensor of claim1, wherein the number of measured outlet process variables include: anoutlet temperature of the hot fluid of the heat exchanger; an outlettemperature of the cold fluid of the heat exchanger; an outlet pressureof the hot fluid of the heat exchanger; an outlet pressure of the coldfluid of the heat exchanger; an outlet flow rate of the hot fluid of theheat exchanger; and an outlet flow rate of the cold fluid of the heatexchanger.
 4. The inferential sensor of claim 1, wherein the internalparameters of the heat exchanger include a heat transfer coefficient ofthe heat exchanger.
 5. The inferential sensor of claim 1, wherein theinternal parameters of the heat exchanger include metal temperatures ofa heat exchange surface of the heat exchanger.
 6. The inferential sensorof claim 1, wherein the processor is configured to execute theinstructions calculate an efficiency of the heat exchanger using theinternal parameters of the heat exchanger and the number of measuredprocess variables of the heat exchanger.
 7. The inferential sensor ofclaim 1, wherein the predicted number of outlet process variablesinclude: a predicted outlet temperature of the hot fluid of the heatexchanger; a predicted outlet temperature of the cold fluid of the heatexchanger; a predicted outlet pressure of the hot fluid of the heatexchanger; a predicted outlet pressure of the cold fluid of the heatexchanger; a predicted outlet flow rate of the hot fluid of the heatexchanger; and a predicted outlet flow rate of the cold fluid of theheat exchanger.
 8. The inferential sensor of claim 1, wherein thedynamic differential model includes a heat and mass balance of thenumber of measured inlet process variables and the number of measuredoutlet process variables, wherein the number of measured inlet processvariables and the number of measured outlet process variables changewith time, and wherein: the heat and mass balance determines a metaltemperature of the heat exchange surface of the heat exchanger; and heattransfer from the hot fluid of the heat exchanger to the cold fluid ofthe heat exchanger is determined by a logarithmic mean temperaturebetween a temperature of the hot fluid, the metal temperature of theheat exchange surface of the heat exchanger, and a temperature of thecold fluid of the heat exchanger.
 9. The inferential sensor of claim 1,wherein a nominal heat exchanger has a nominal heat transfercoefficient.
 10. The inferential sensor of claim 1, wherein the heatexchanger has a heat transfer coefficient.
 11. The inferential sensor ofclaim 1, wherein the controller is part of a heat pump.
 12. Theinferential sensor of claim 1, wherein the controller is part of anair-conditioner.
 13. The inferential sensor of claim 1, wherein theprocessor is configured to execute the instructions while the heatexchanger is operating.
 14. A method for an inferential sensor forinternal heat exchanger parameters, comprising: receiving a number ofmeasured process variables of the heat exchanger, including: a number ofmeasured inlet process variables; and a number of measured outletprocess variables of the heat exchanger; predicting internal parametersof the heat exchanger and a number of outlet process variables by adynamic differential model using a heat and mass balance of the numberof measured inlet process variables and the number of measured outletprocess variables of the heat exchanger; comparing the number ofmeasured outlet process variables with the number of predicted outletprocess variables; and updating, based on the comparison, the internalparameters of the heat exchanger.
 15. The method of claim 14, whereinupdating the internal parameters of the heat exchanger include updatinga heat transfer coefficient of the heat exchanger.
 16. The method ofclaim 14, wherein updating the internal parameters of the heat exchangerinclude updating metal temperatures of a heat exchange surface of theheat exchanger.
 17. The method of claim 14, wherein the method iscontinuously repeated.
 18. A system for an inferential sensor forinternal heat exchanger parameters, comprising: a heat pump; a heatexchanger; a controller configured to: receive a number of measuredprocess variables of the heat exchanger, including: a number of measuredinlet process variables of the heat exchanger; a number of measuredoutlet process variables of the heat exchanger; predict, using a dynamicdifferential model including the number of measured inlet processvariables: internal parameters of the heat exchanger; and a number ofoutlet process variables of the heat exchanger; compare the number ofmeasured outlet process variables with the number of predicted outletprocess variables; update, based on the comparison, the internalparameters of the heat exchanger.
 19. The system of claim 18, whereinthe heat exchanger is a liquid-liquid heat exchanger.
 20. The system ofclaim 18, wherein the heat exchanger is a phase change heat exchanger.